Statistics 3022代写、R语言代做、代写Sparrows data、R程序设计调试

日期：2019-10-03 10:45

Statistics 3022 Assignment 2

1. (True False questions) Textbook Problem 2.2, 2.4, 2.5, 2.6, 2.7 (page 81). If it is false, give a reason.

2. (Multiple choice question) Textbook Problem 2.8 (page 81)

For Problem 3 through 5: Priscilla Erickson from Kenyon College collected data on a stratified random

sample of 116 Savannah sparrows at Kent Island, New Brunswick, Canada. The weight (in grams) and wing

length (in mm) were obtained for birds from nests that were reduced, controlled, or enlarged. You are only

allowed to use the provided R outputs.(Sparrows data as in the previous homework)

summary(model)

##

## Call:

## lm(formula = Weight ~ WingLength, data = data_Sparrows)

##

## Residuals:

## Min 1Q Median 3Q Max

## -3.5440 -0.9935 0.0809 1.0559 3.4168

##

## Coefficients:

## Estimate Std. Error t value Pr(>|t|)

## (Intercept) 1.36549 0.95731 1.426 0.156

## WingLength 0.46740 0.03472 13.463 <2e-16 ***

## ---

## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##

## Residual standard error: 1.4 on 114 degrees of freedom

## Multiple R-squared: 0.6139, Adjusted R-squared: 0.6105

## F-statistic: 181.3 on 1 and 114 DF, p-value: < 2.2e-16

3. (Sparrow) Perform t-test and conclude whether the slope of the least squares regression line for prediction

Weight from WingLength is non-zero. Use summary(model) output above.

(a) State the null and alternative hypothesis.

(b) Identify and interpret the t-test statistic and p-value from R output above.

(c) Draw the conclusion with α = 0.05 and interpret it in the context of the problem.

4. (Sparrow) Use anova(model) output to calculate Multiple R-squared ( in R output above.) and interpret

it. Show your work. (Hint: R-squared = (Variability explained by model)/(Total variability in y))

5. (Sparrow) Use the following outputs only to construct 95% confidence interval of β1 (slope) of the

model and interpret in the context of the problem. Does it agree with your conclusion from Problem 3

above? Explain.

(n<-nrow(data_Sparrows))#number of observations

## [1] 116

qt(0.975, n-2)

## [1] 1.980992

1

qt(0.95, n-2)

## [1] 1.65833

For Problem 6 through 8 In the website and online forum RateMyProfessors.com, students rate and comment

on their instructors. Launched in 1999, the site includes millions of ratings on thousands of instructors.

The data includes the summaries of the ratings of 364 instructors and a large campus in the Midwest

(Bleske-Rechek and Fritsch, 2011). Each instructor included in the data had at least 10 ratings over a several

year period. Students provided ratings of 1-5 on quality, helpfulness, clarity, easiness of instructor’s courses.

Use R command below to import the data set. In this problem we want to predict quality rating from

helpfulness rating. You may use predict() or manually calculate each answer based on R regression model

summary. rmp<-read.csv(“http://users.stat.umn.edu/~sandy/alr4ed/data/Rateprof.csv”)

6. (Rmp) Use R to construct the estimated regression model with quality as the response variable and

helpfulness as the predictor. Based on your model, What is the estimated quality rating for a randomly

selected professor if his/her helpfulness rating is 3.

7. (Rmp) Use R to construct a 95% confidence interval to estimate the mean quality rating if helpfulness

rating is 3. Interpret the result.

8. (Rmp) Use R to construct a 95% prediction interval to estimate Professor John’s quality rating if his

helpfulness rating is 3. Interpret the result.

9. Textbook Problem 3.1 (page 149)

10. Textbook Problem 3.2 (page 149)

11. Textbook Problem 3.11 (page 151 and 152).

12. Textbook Problems 3.3 and 3.15 (page 149 and 153)

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